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Extra resources for Automated face analysis : emerging technologies and research
PhD thesis, MIT, AI Lab, Cambridge. , & Poggio, T. (1998). Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 39–51. , & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, (pp. 511-518). , & Jones, M. (2002). Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing System, 14, MIT Press, Cambridge.
Fig. 15. Some examples of the correctly detected eyes in AR-564 face database Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. 16. Some examples of the falsely detected eyes in AR-564 face database Fig. 17. Four examples of the falsely detected eye correction In this section, we use AdaBoost with the modified census transform (MCT) (Freund and Schapire, 1999; Fröba and Ernst, 2004) due to its simplicity of learning and high speed of detection.
6 shows some captured images of the cam. 2 shows the FAR of the facial disguise discrimination using one image and the image sequence. These result shows that using image sequence with Binomial distribution’s CDF of FAR is accurate and reliable for the facial disguise discrimination. 6 Cnclusion In this chapter, we explained a robust face detection algorithm using difference of pyramid (DoP) images and face certainty map (FCM). The experimental results showed that the reduction of FAR is ten times better than existing cascade AdaBoost detector while keeping detection rate and detection time almost the same.